To facilitate high-dimensional KNN queries,based on techniques of approximate vector presentation and one-dimensional transformation,an optimal index is proposed,namely Bit-Code based iDistance(BC-iDistance).To overco...To facilitate high-dimensional KNN queries,based on techniques of approximate vector presentation and one-dimensional transformation,an optimal index is proposed,namely Bit-Code based iDistance(BC-iDistance).To overcome the defect of much information loss for iDistance in one-dimensional transformation,the BC-iDistance adopts a novel representation of compressing a d-dimensional vector into a two-dimensional vector,and employs the concepts of bit code and one-dimensional distance to reflect the location and similarity of the data point relative to the corresponding reference point respectively.By employing the classical B+tree,this representation realizes a two-level pruning process and facilitates the use of a single index structure to further speed up the processing.Experimental evaluations using synthetic data and real data demonstrate that the BC-iDistance outperforms the iDistance and sequential scan for KNN search in high-dimensional spaces.展开更多
基金Sponsored by the National High Technology Research and Development Program of China (863 Program)(Grant No.[2005]555)
文摘To facilitate high-dimensional KNN queries,based on techniques of approximate vector presentation and one-dimensional transformation,an optimal index is proposed,namely Bit-Code based iDistance(BC-iDistance).To overcome the defect of much information loss for iDistance in one-dimensional transformation,the BC-iDistance adopts a novel representation of compressing a d-dimensional vector into a two-dimensional vector,and employs the concepts of bit code and one-dimensional distance to reflect the location and similarity of the data point relative to the corresponding reference point respectively.By employing the classical B+tree,this representation realizes a two-level pruning process and facilitates the use of a single index structure to further speed up the processing.Experimental evaluations using synthetic data and real data demonstrate that the BC-iDistance outperforms the iDistance and sequential scan for KNN search in high-dimensional spaces.
